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        <a href="deepbelief-module.html">Package&nbsp;deepbelief</a> ::
        <a href="deepbelief.gaussianrbm-module.html">Module&nbsp;gaussianrbm</a> ::
        Class&nbsp;GaussianRBM
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class GaussianRBM</h1><p class="nomargin-top"><span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM">source&nbsp;code</a></span></p>
<pre class="base-tree">
<a href="deepbelief.abstractbm.AbstractBM-class.html">abstractbm.AbstractBM</a> --+
                        |
                       <strong class="uidshort">GaussianRBM</strong>
</pre>

<dl><dt>Known Subclasses:</dt>
<dd>
      <ul class="subclass-list">
<li><a href="deepbelief.mixbm.MixBM-class.html">mixbm.MixBM</a></li><li>, <a href="deepbelief.basebm.BaseBM-class.html">basebm.BaseBM</a></li>  </ul>
</dd></dl>

<hr />
<p>An implementation of the Gaussian RBM with continuous visible 
  nodes.</p>
  <p>References: Salakhutdinov, R. (2009). <i>Learning Deep Generative 
  Models.</i></p>

<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
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  <td align="left" colspan="2" class="table-header">
    <span class="table-header">Instance Methods</span></td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
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          <td><span class="summary-sig"><a href="deepbelief.gaussianrbm.GaussianRBM-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">num_visibles</span>,
        <span class="summary-sig-arg">num_hiddens</span>)</span><br />
      Initializes common parameters of Boltzmann machines.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.__init__">source&nbsp;code</a></span>
            
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      <span class="summary-type">matrix</span>
    </td><td class="summary">
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        <tr>
          <td><span class="summary-sig"><a href="deepbelief.gaussianrbm.GaussianRBM-class.html#backward" class="summary-sig-name">backward</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">Y</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">X</span>=<span class="summary-sig-default">None</span>)</span><br />
      Conditionally samples the visible units.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.backward">source&nbsp;code</a></span>
            
          </td>
        </tr>
      </table>
      
    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">matrix</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
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          <td><span class="summary-sig"><a href="deepbelief.gaussianrbm.GaussianRBM-class.html#forward" class="summary-sig-name">forward</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">X</span>=<span class="summary-sig-default">None</span>)</span><br />
      Conditionally samples the hidden units.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.forward">source&nbsp;code</a></span>
            
          </td>
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  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="deepbelief.abstractbm.AbstractBM-class.html">abstractbm.AbstractBM</a></code></b>:
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#clear">clear</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#estimate_log_likelihood">estimate_log_likelihood</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#estimate_log_partition_function">estimate_log_partition_function</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#sample">sample</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#train">train</a></code>
      </p>
    </td>
  </tr>
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<!-- ==================== CLASS VARIABLES ==================== -->
<a name="section-ClassVariables"></a>
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  <td align="left" colspan="2" class="table-header">
    <span class="table-header">Class Variables</span></td>
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    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="deepbelief.abstractbm.AbstractBM-class.html">abstractbm.AbstractBM</a></code></b>:
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#GIBBS">GIBBS</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#HMC">HMC</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#MF">MF</a></code>
      </p>
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<!-- ==================== INSTANCE VARIABLES ==================== -->
<a name="section-InstanceVariables"></a>
<table class="summary" border="1" cellpadding="3"
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  <td align="left" colspan="2" class="table-header">
    <span class="table-header">Instance Variables</span></td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">real</span>
    </td><td class="summary">
        <a name="sigma"></a><span class="summary-name">sigma</span><br />
      controls the variance of conditional distribution of the visible 
      units
    </td>
  </tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code><a href="deepbelief.abstractbm.AbstractBM-class.html">abstractbm.AbstractBM</a></code></b>:
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#W">W</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#X">X</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#Y">Y</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#b">b</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#c">c</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#cd_steps">cd_steps</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#learning_rate">learning_rate</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#lf_adaptive">lf_adaptive</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#lf_step_size">lf_step_size</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#lf_steps">lf_steps</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#momentum">momentum</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#persistent">persistent</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#sampling_method">sampling_method</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#sparseness">sparseness</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#sparseness_target">sparseness_target</a></code>,
      <code><a href="deepbelief.abstractbm.AbstractBM-class.html#weight_decay">weight_decay</a></code>
      </p>
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<!-- ==================== METHOD DETAILS ==================== -->
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  <td align="left" colspan="2" class="table-header">
    <span class="table-header">Method Details</span></td>
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<a name="__init__"></a>
<div>
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<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">num_visibles</span>,
        <span class="sig-arg">num_hiddens</span>)</span>
    <br /><em class="fname">(Constructor)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.__init__">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Initializes common parameters of Boltzmann machines.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>num_visibles</code></strong> - number of visible units</li>
        <li><strong class="pname"><code>num_hiddens</code></strong> - number of hidden units</li>
    </ul></dd>
    <dt>Overrides:
        <a href="deepbelief.abstractbm.AbstractBM-class.html#__init__">abstractbm.AbstractBM.__init__</a>
        <dd><em class="note">(inherited documentation)</em></dd>
    </dt>
  </dl>
</td></tr></table>
</div>
<a name="backward"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">backward</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">Y</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">X</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.backward">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Conditionally samples the visible units. If <code>Y</code> or 
  <code>X</code> is given, the state of the Boltzmann machine is changed 
  prior to sampling.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>Y</code></strong> - states of hidden units</li>
        <li><strong class="pname"><code>X</code></strong> - states of visible units</li>
    </ul></dd>
    <dt>Returns: matrix</dt>
        <dd>a matrix containing states for the visible units</dd>
    <dt>Overrides:
        <a href="deepbelief.abstractbm.AbstractBM-class.html#backward">abstractbm.AbstractBM.backward</a>
        <dd><em class="note">(inherited documentation)</em></dd>
    </dt>
  </dl>
</td></tr></table>
</div>
<a name="forward"></a>
<div>
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       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">forward</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">X</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.gaussianrbm-pysrc.html#GaussianRBM.forward">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Conditionally samples the hidden units. If no input is given, the 
  current state of the visible units is used.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>X</code></strong> - states of visible units</li>
    </ul></dd>
    <dt>Returns: matrix</dt>
        <dd>a matrix containing states for the hidden units</dd>
    <dt>Overrides:
        <a href="deepbelief.abstractbm.AbstractBM-class.html#forward">abstractbm.AbstractBM.forward</a>
        <dd><em class="note">(inherited documentation)</em></dd>
    </dt>
  </dl>
</td></tr></table>
</div>
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